Overview

When you publish manuscripts based on data generated at our facility, we would greatly appreciate an acknowledgement of our efforts. Please cite our facility as follows (for example):

Basic processing of the raw data were performed by the University of Illinois at Chicago Research Informatics Core (UICRIC).

We adhere to a general policy for acknowledgements and authorship as established by the Association for Biomolecular Resource Facilities (ABRF) , and we support the following statement from the ABRF.

The existence of core facilities depends in part on proper acknowledgment in publications. This is an important metric of the value of most core facilities. Proper acknowledgment of core facilities enables them to obtain financial and other support so that they may continue to provide their essential services in the best ways possible. It also helps core personnel to advance in their careers, adding to the overall health of the core facility.

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File Description Type
taxa_raw_counts.zip ZIP archive of taxonomic summaries from phylum to species level result - raw sequence counts result
ko_raw_counts.xlsx Excel spreadsheet of funtional gene summaries - raw sequence counts result
metagenomic_assembly.zip ZIP compressed FASTA file of all contigs in the assembly result
contig_counts.txt Raw read counts for assembled contigs result
orf_counts.txt Raw read counts for predicted ORFs result
contig_data.txt Details and normalized read counts (RPKM) of the contigs result
contig_annotations.txt Gene annotations of predicted ORFs, as TSV file result
annotation_results.zip ZIP file of annotation results as and GFF format with FASTA files of nucleotide and amino acid sequences for each ORF result
bins_data.txt Details the metagenomic bins result

Sample list
Sample OriginalID
SRR22405441 SRR22405441
SRR22405442 SRR22405442
SRR22405445 SRR22405445
SRR22405453 SRR22405453
SRR22405460 SRR22405460
SRR22405461 SRR22405461
SRR22405467 SRR22405467
SRR22405532 SRR22405532
SRR22405538 SRR22405538
SRR22405544 SRR22405544

Method: cutadaptMartin, M. (2011). Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal, 17(1):10-12. doi:https://doi.org/10.14806/ej.17.1.200 (version: 4.4)

Sequencing trimming using cutadapt

Custom Parameters
  • --report = minimal
  • -m = 70
  • --trim-n
  • --max-n = 1
  • -b = file:/mmfs1/projects/rrc_shared/common/references/adapters/illumina_adapters.fa
Method: Quality trimming

Quality trimming based on quality threshold and length parameters.

Custom Parameters
  • min length = 70
Method: Adapter trimming

Adapter/primer sequences were trimmed from the reads.

Custom Parameters
  • adapter = AGATCGGAAGAGCACACGTCTGAACTCCAGTCA
  • adapter = AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGT
  • adapter = TGACTGGAGTTCAGACGTGTGCTCTTCCGATCT
  • adapter = ACACTCTTTCCCTACACGACGCTCTTCCGATCT
Method: Abiguous nucleotide trimming

Ambiguous nucleotides (N) were trimmed from the ends and reads with internal ambiguous nucleotides were discarded.

Custom Parameters
  • max-n = 1

Figure 1 . Trimming results

Table 1 . Trimming statistics

Table 1 . Trimming statistics
Sample Trim passed Ambig dropped Trim filter dropped Trim length dropped Percent passed Mean Length Mean Trimmed Length Quality trimmed bp
SRR22405441 998916 1084 0 0 99.89% 302.0 301.6 0
SRR22405442 999093 907 0 0 99.91% 302.0 301.6 0
SRR22405445 999098 902 0 0 99.91% 302.0 301.6 0
SRR22405453 999209 791 0 0 99.92% 302.0 301.6 0
SRR22405460 999065 935 0 0 99.91% 302.0 301.6 0
SRR22405461 999104 896 0 0 99.91% 302.0 301.6 0
SRR22405467 999137 863 0 0 99.91% 302.0 301.6 0
SRR22405532 999248 752 0 0 99.92% 302.0 301.6 0
SRR22405538 999247 753 0 0 99.92% 302.0 301.6 0
SRR22405544 999069 931 0 0 99.91% 302.0 301.6 0

  • Denovo genome assembly using short read sequence data

Method: Ultra-fast and memory-efficient (meta-)genome assemblerLi, D., Liu, C-M., Luo, R., Sadakane, K., and Lam, T-W., (2015) MEGAHIT: An ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics, https://doi.org/10.1093/bioinformatics/btv033Li, D., Luo, R., Liu, C.M., Leung, C.M., Ting, H.F., Sadakane, K., Yamashita, H. and Lam, T.W., 2016. MEGAHIT v1.0: A Fast and Scalable Metagenome Assembler driven by Advanced Methodologies and Community Practices. Methods.

MEGAHIT is an ultra-fast and memory-efficient NGS assembler. It is optimized for metagenomes, but also works well on generic single genome assembly (small or mammalian size) and single-cell assembly.

Figure 1 . Coverage plot

Table 1 . Assembly summaries by stage

Table 1 . Assembly summaries by stage
Stage Count TotalLength Longest N50 N75 N90 L50 L75 L90
Raw assembly 98798 133043265 349967 4515 773 423 3297 25039 60984
Entropy filtered 17838 93487326 349967 17101 3588 1591 836 4192 10290
Alignment filtered 17838 93487326 349967 17101 3588 1591 836 4192 10290

Table 2 . Basic contig statistics, all contigs

Table 2 . Basic contig statistics, all contigs
#ID length GC coverage S_2mer S_3mer S_4mer
k141_79676 349967 48.068 71.95 0.995 0.992 0.988
k141_56136 343211 43.778 14.91 0.991 0.987 0.984
k141_20564 342700 47.468 52.48 0.995 0.992 0.988
k141_40847 334399 46.144 42.69 0.995 0.992 0.988
k141_43827 330256 56.528 43.89 0.985 0.976 0.971
k141_13782 299237 42.963 103.05 0.989 0.986 0.983
k141_76730 298100 46.238 47.34 0.994 0.991 0.987
k141_22633 297381 42.899 23.71 0.989 0.985 0.982
k141_60340 294754 53.046 25.53 0.995 0.991 0.988
k141_78371 290324 43.567 15.99 0.992 0.988 0.985
k141_71004 284177 59.188 15.93 0.980 0.976 0.971
k141_90065 260876 51.135 89.65 0.996 0.993 0.990
k141_78110 260643 47.584 52.42 0.996 0.992 0.989
k141_70850 254313 51.474 85.44 0.996 0.993 0.989
k141_75623 250564 60.446 19.12 0.977 0.972 0.968
k141_96938 248942 44.240 18.86 0.991 0.986 0.982
k141_81434 247194 49.716 81.95 0.996 0.993 0.990
k141_45924 244785 45.550 45.19 0.993 0.990 0.986
k141_83169 237758 59.367 18.46 0.980 0.976 0.971
k141_30602 231031 43.386 20.93 0.990 0.986 0.983
k141_2697 219676 44.398 17.06 0.991 0.986 0.982
k141_45404 216979 42.710 95.56 0.987 0.984 0.981
k141_84618 215400 45.105 48.75 0.991 0.988 0.984
k141_46326 214267 51.571 24.87 0.995 0.991 0.987
k141_50301 212672 45.597 23.28 0.994 0.989 0.985
k141_53297 212293 42.071 14.21 0.988 0.985 0.982
k141_32521 210510 48.181 62.22 0.996 0.992 0.989
k141_2074 206891 52.658 24.65 0.994 0.990 0.986
k141_32426 201433 47.380 53.60 0.996 0.992 0.989
k141_47607 197638 42.358 15.04 0.988 0.985 0.982
k141_81143 187223 52.728 25.19 0.995 0.990 0.987
k141_16896 185271 47.826 44.07 0.996 0.993 0.989
k141_50278 183654 59.867 17.63 0.979 0.974 0.970
k141_26236 181034 55.255 36.49 0.987 0.979 0.973
k141_38219 178446 59.119 18.46 0.980 0.975 0.970
k141_33725 176101 52.275 24.66 0.994 0.990 0.986
k141_47217 175853 45.180 13.75 0.995 0.991 0.987
k141_96940 174227 44.188 16.41 0.991 0.987 0.983
k141_5901 173023 42.926 89.94 0.989 0.986 0.983
k141_61816 171919 52.663 64.32 0.996 0.993 0.989
k141_89124 171703 44.804 107.49 0.992 0.989 0.986
k141_96828 170655 55.980 15.11 0.991 0.986 0.982
k141_26037 170476 52.318 24.92 0.996 0.992 0.989
k141_37310 169718 56.888 15.50 0.989 0.985 0.980
k141_75657 166254 54.122 14.72 0.994 0.991 0.987
k141_39818 165793 43.850 24.56 0.990 0.985 0.982
k141_41490 164124 44.333 20.30 0.991 0.986 0.983
k141_77004 163397 43.819 15.28 0.991 0.987 0.984
k141_12153 158681 42.615 17.23 0.988 0.984 0.981
k141_80197 158391 55.843 15.79 0.988 0.984 0.980
k141_22099 157936 51.005 20.85 0.994 0.991 0.987
k141_31338 156971 59.596 14.16 0.979 0.974 0.970
k141_94318 155923 43.156 23.90 0.989 0.985 0.981
k141_51250 154384 46.888 51.19 0.995 0.992 0.988
k141_3877 153662 43.432 90.19 0.990 0.987 0.984
k141_86019 153142 42.690 15.93 0.989 0.984 0.981
k141_32115 152608 42.626 116.29 0.989 0.986 0.983
k141_83416 151527 43.632 15.37 0.990 0.987 0.983
k141_48174 151251 45.147 15.59 0.993 0.989 0.985
k141_27394 150924 44.891 19.94 0.993 0.989 0.985
k141_96398 149946 59.731 15.46 0.979 0.974 0.969
k141_13336 149678 42.159 102.80 0.988 0.985 0.981
k141_43015 149580 47.624 11.11 0.996 0.993 0.990
k141_30027 145668 43.579 107.52 0.990 0.987 0.983
k141_72134 142594 51.029 82.99 0.995 0.992 0.988
k141_44535 142365 41.814 106.16 0.987 0.984 0.981
k141_99310 142287 43.939 98.40 0.991 0.988 0.984
k141_51821 141763 42.184 105.33 0.987 0.984 0.980
k141_53499 136465 56.687 39.04 0.985 0.976 0.970
k141_22152 135305 59.073 17.52 0.979 0.974 0.970
k141_94747 135091 56.597 17.36 0.987 0.982 0.979
k141_27962 133070 49.678 75.76 0.995 0.992 0.988
k141_81870 132617 45.896 22.78 0.993 0.990 0.987
k141_92764 132166 43.645 105.77 0.989 0.986 0.982
k141_23307 132128 42.288 23.54 0.987 0.983 0.979
k141_13083 131559 55.999 42.27 0.985 0.977 0.971
k141_50681 130310 40.701 93.16 0.984 0.981 0.978
k141_59960 130099 43.392 22.81 0.990 0.986 0.982
k141_45737 128383 41.062 21.22 0.984 0.980 0.977
k141_85540 128221 50.585 18.59 0.993 0.989 0.985
k141_57999 125307 50.084 28.02 0.996 0.992 0.989
k141_28310 123846 43.918 17.95 0.992 0.988 0.985
k141_89872 123773 51.598 20.19 0.992 0.988 0.984
k141_50775 122240 58.581 15.27 0.981 0.977 0.972
k141_55237 122187 49.666 17.58 0.992 0.988 0.984
k141_15364 122120 50.239 19.00 0.994 0.990 0.986
k141_10275 121245 49.061 87.37 0.997 0.994 0.990
k141_81344 121088 47.904 50.61 0.996 0.993 0.989
k141_31502 119854 56.956 15.03 0.987 0.984 0.979
k141_60424 119376 41.513 21.65 0.985 0.982 0.978
k141_64681 119182 46.954 21.94 0.995 0.992 0.989
k141_2630 115178 42.488 18.40 0.988 0.985 0.981
k141_24426 113096 52.944 86.43 0.995 0.992 0.987
k141_6559 112992 50.960 82.16 0.995 0.992 0.987
k141_25594 111928 42.784 19.77 0.988 0.983 0.980
k141_87070 111178 57.975 13.67 0.985 0.981 0.977
k141_54479 110986 58.216 13.63 0.983 0.978 0.974
k141_50200 110719 42.642 23.86 0.989 0.985 0.982
k141_56708 109773 47.991 22.59 0.995 0.991 0.988
k141_88900 108052 47.107 50.54 0.995 0.991 0.987
k141_54186 106536 43.321 114.54 0.990 0.986 0.983
k141_5277 105585 53.258 26.02 0.996 0.992 0.988
k141_8948 105435 42.683 93.97 0.988 0.985 0.982
k141_82689 105003 53.173 25.54 0.995 0.991 0.987
k141_24685 104179 48.006 49.05 0.996 0.992 0.987
k141_94157 104038 51.698 87.94 0.997 0.994 0.990
k141_57009 103194 43.400 22.96 0.991 0.987 0.985
k141_93908 103058 42.712 21.30 0.988 0.983 0.980
k141_17039 102470 49.339 77.09 0.996 0.993 0.989
k141_95176 102259 44.355 12.85 0.993 0.989 0.985
k141_18138 102246 51.984 24.35 0.996 0.992 0.989
k141_69352 100834 46.324 47.84 0.993 0.989 0.985
k141_48094 100722 44.054 20.10 0.991 0.986 0.983
k141_69415 100069 42.456 110.45 0.987 0.984 0.981
k141_53630 98611 41.167 19.03 0.986 0.982 0.979
k141_37915 98181 49.633 22.70 0.996 0.993 0.990
k141_6681 97113 49.494 16.72 0.995 0.991 0.988
k141_88514 96475 43.396 22.20 0.990 0.986 0.982
k141_84639 96469 51.144 19.29 0.993 0.990 0.986
k141_13507 95969 54.683 35.00 0.988 0.977 0.971
k141_9597 95611 42.234 20.42 0.986 0.982 0.979
k141_14584 93673 44.244 15.48 0.993 0.989 0.985
k141_35809 92350 54.744 14.96 0.992 0.987 0.983
k141_67258 92210 44.014 118.21 0.992 0.989 0.986
k141_81911 92097 46.030 44.27 0.993 0.990 0.986
k141_69746 91819 51.926 25.07 0.994 0.990 0.986
k141_22542 91155 45.924 48.94 0.992 0.988 0.984
k141_14096 90938 44.397 17.11 0.990 0.985 0.981
k141_54639 90881 48.547 94.12 0.996 0.993 0.989
k141_89902 90510 56.723 41.20 0.983 0.974 0.968
k141_33244 90004 56.279 15.62 0.990 0.985 0.981
k141_35023 89692 53.772 12.18 0.994 0.991 0.988
k141_14260 89188 44.223 90.99 0.991 0.988 0.985
k141_28219 88763 57.652 15.71 0.988 0.984 0.979
k141_89207 87882 42.040 111.44 0.987 0.984 0.981
k141_39746 87627 49.385 80.60 0.996 0.993 0.989
k141_7284 87173 44.237 14.92 0.992 0.988 0.984
k141_77730 86661 43.464 16.91 0.990 0.985 0.982
k141_95440 86515 53.057 23.06 0.993 0.989 0.985
k141_60582 86454 41.184 12.34 0.986 0.982 0.979
k141_3015 86218 51.451 75.94 0.995 0.992 0.987
k141_1375 85903 39.908 18.90 0.980 0.977 0.974
k141_92363 85546 54.970 15.49 0.988 0.983 0.979
k141_77497 85296 55.862 36.58 0.985 0.977 0.970
k141_22752 85138 45.083 110.88 0.992 0.988 0.985
k141_57203 84646 51.024 24.16 0.996 0.992 0.988
k141_40938 84614 35.912 49.60 0.963 0.958 0.954
k141_30743 83402 43.096 21.65 0.989 0.984 0.979
k141_67527 82819 55.090 34.74 0.991 0.987 0.984
k141_98037 82488 41.452 92.74 0.986 0.984 0.981
k141_26772 81974 55.830 34.04 0.986 0.977 0.971
k141_56229 81821 42.415 22.68 0.987 0.983 0.979
k141_53953 81384 64.865 13.66 0.961 0.957 0.951
k141_91152 81285 53.550 22.95 0.992 0.989 0.984
k141_77526 80719 42.082 14.26 0.988 0.984 0.981
k141_39720 80405 58.367 15.31 0.981 0.976 0.971
k141_60627 80387 56.054 14.66 0.986 0.982 0.978
k141_3576 80201 40.598 17.87 0.983 0.980 0.977
k141_34055 79781 41.907 96.02 0.986 0.983 0.980
k141_95640 78463 45.776 18.98 0.994 0.989 0.984
k141_89263 78312 43.320 12.13 0.990 0.987 0.983
k141_43433 78195 46.717 51.37 0.995 0.991 0.987
k141_88748 78120 41.852 22.71 0.986 0.981 0.978
k141_81468 77910 45.975 15.85 0.993 0.989 0.986
k141_54249 77458 44.700 18.73 0.993 0.989 0.985
k141_51897 76803 49.157 44.86 0.996 0.993 0.988
k141_18113 76740 40.658 11.50 0.984 0.981 0.979
k141_32478 76690 43.735 15.35 0.990 0.987 0.983
k141_71745 76151 55.318 43.19 0.987 0.978 0.972
k141_79638 76009 42.990 111.33 0.989 0.986 0.983
k141_52953 75523 56.493 37.17 0.986 0.976 0.970
k141_51909 75121 47.984 22.32 0.995 0.992 0.989
k141_64710 75099 53.561 13.97 0.993 0.988 0.984
k141_60301 74975 49.931 96.84 0.997 0.995 0.991
k141_26687 74893 56.276 42.15 0.983 0.974 0.968
k141_27493 74523 43.733 15.09 0.991 0.987 0.983
k141_81055 74238 41.145 24.36 0.984 0.979 0.976
k141_14149 74029 46.715 14.92 0.996 0.992 0.989
k141_25493 72914 54.493 48.30 0.990 0.981 0.975
k141_9322 71637 43.108 112.67 0.990 0.987 0.984
k141_48164 71566 59.732 17.27 0.979 0.974 0.968
k141_32249 69867 54.332 40.17 0.988 0.981 0.975
k141_65677 69627 55.632 36.74 0.988 0.982 0.977
k141_18413 69550 55.449 37.69 0.988 0.979 0.973
k141_12827 69444 43.459 14.16 0.991 0.988 0.984
k141_53012 69201 42.199 110.77 0.987 0.983 0.980
k141_51651 69195 44.114 23.43 0.990 0.987 0.984
k141_88445 68840 45.521 50.88 0.993 0.990 0.986
k141_10095 68757 41.164 21.55 0.983 0.978 0.975
k141_36755 68739 47.573 74.20 0.995 0.992 0.988
k141_27572 68724 44.840 92.96 0.991 0.987 0.983
k141_86462 68686 59.672 13.52 0.979 0.975 0.970
k141_52365 68586 43.108 11.02 0.990 0.986 0.983
k141_93085 68551 56.476 15.75 0.991 0.987 0.982
k141_82537 68049 52.543 22.39 0.993 0.990 0.986
k141_33726 68038 63.279 16.27 0.967 0.963 0.958
k141_5417 67970 55.463 14.28 0.991 0.988 0.983
k141_33172 67697 56.109 14.24 0.990 0.986 0.982
k141_77538 67417 44.078 22.31 0.990 0.985 0.981
k141_76742 67061 52.691 24.95 0.996 0.992 0.988
k141_12466 66824 53.011 22.45 0.992 0.989 0.985
k141_12476 66786 46.450 12.08 0.993 0.991 0.988
k141_98828 66704 43.903 119.33 0.991 0.988 0.985
k141_85295 65879 42.655 96.61 0.988 0.985 0.981
k141_42895 65839 40.614 15.60 0.982 0.978 0.976
k141_88697 65573 56.209 39.65 0.986 0.977 0.970
k141_46439 65540 44.173 13.94 0.991 0.988 0.985
k141_71615 65169 43.871 17.58 0.992 0.988 0.985
k141_84968 65103 44.376 99.09 0.992 0.988 0.985
k141_99782 64722 42.024 13.03 0.989 0.986 0.984
k141_93010 64415 52.750 11.45 0.992 0.988 0.984
k141_88706 64269 54.319 16.12 0.993 0.989 0.985
k141_90134 63846 47.496 53.99 0.995 0.992 0.988
k141_99790 63740 53.106 12.14 0.992 0.989 0.985
k141_57886 63526 50.530 25.35 0.995 0.992 0.988
k141_1867 63424 56.644 39.72 0.982 0.973 0.966
k141_47519 62988 44.342 15.47 0.991 0.987 0.984
k141_16433 62706 42.956 96.05 0.990 0.988 0.985
k141_25300 62664 43.993 42.53 0.990 0.987 0.984
k141_30595 62354 44.894 12.89 0.993 0.989 0.985
k141_47763 61911 43.231 110.39 0.988 0.985 0.982
k141_870 61831 46.643 102.29 0.994 0.991 0.987
k141_24185 61337 56.460 35.08 0.986 0.978 0.972
k141_88261 61296 55.284 33.94 0.987 0.978 0.972
k141_32084 60876 52.181 24.33 0.994 0.989 0.985
k141_22355 60876 52.592 13.67 0.995 0.990 0.986
k141_8243 60458 41.953 86.84 0.987 0.985 0.981
k141_37615 60443 56.582 34.53 0.986 0.978 0.972
k141_32062 60410 53.038 12.68 0.992 0.988 0.984
k141_17214 60064 58.724 14.13 0.983 0.978 0.973
k141_27961 59867 43.206 23.65 0.989 0.985 0.981
k141_1213 59800 45.682 14.45 0.995 0.990 0.986
k141_62303 59776 43.967 16.20 0.991 0.987 0.983
k141_29045 59458 46.885 30.01 0.995 0.993 0.989
k141_51396 59372 55.351 14.67 0.991 0.987 0.983
k141_13828 59208 42.369 21.43 0.987 0.984 0.980
k141_3440 59040 44.245 14.19 0.993 0.989 0.985
k141_62766 58995 39.720 12.92 0.981 0.977 0.974
k141_90926 58841 43.057 23.51 0.989 0.984 0.981
k141_4477 58656 49.441 75.62 0.995 0.992 0.988
k141_15904 58605 44.111 11.81 0.990 0.986 0.982
k141_54604 58509 42.349 169.23 0.985 0.980 0.974
k141_64794 58491 47.691 46.68 0.995 0.992 0.987
k141_80692 58481 57.391 45.25 0.984 0.975 0.968
k141_24939 58244 47.500 16.59 0.993 0.990 0.986
k141_79875 58051 52.056 20.77 0.990 0.986 0.981
k141_46259 58003 53.904 14.39 0.990 0.985 0.981
k141_54856 57948 53.117 21.82 0.991 0.987 0.983
k141_82900 57717 59.624 14.82 0.978 0.973 0.968
k141_8847 57599 64.234 12.72 0.963 0.958 0.953
k141_31751 57222 42.730 115.92 0.988 0.985 0.982
k141_29637 56919 56.514 39.31 0.984 0.976 0.969
k141_67635 56866 60.894 16.97 0.976 0.972 0.968
k141_34400 56716 41.692 15.51 0.985 0.981 0.978
k141_28092 56616 56.044 35.57 0.986 0.977 0.971
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k141_2448 3047 52.872 6.64 0.994 0.985 0.973
k141_66865 3046 47.045 5.77 0.988 0.977 0.966
k141_31957 3045 43.810 12.51 0.984 0.977 0.966
k141_22813 3045 51.067 6.31 0.982 0.971 0.959
k141_14661 3045 45.025 7.58 0.990 0.984 0.975
k141_23499 3044 55.618 5.27 0.970 0.963 0.949
k141_49334 3043 41.702 32.91 0.971 0.959 0.947
k141_12987 3043 45.613 7.45 0.990 0.984 0.975
k141_14085 3042 58.481 6.07 0.972 0.959 0.944
k141_1458 3041 48.767 6.26 0.983 0.971 0.960
k141_66712 3039 54.623 6.84 0.989 0.980 0.969
k141_39704 3039 44.620 7.50 0.989 0.984 0.975
k141_46206 3038 55.300 8.36 0.991 0.987 0.979
k141_99207 3037 53.902 14.47 0.987 0.979 0.968
k141_9693 3037 46.526 5.20 0.983 0.970 0.958
k141_84351 3037 52.453 10.92 0.985 0.974 0.963
k141_74134 3037 34.705 5.90 0.959 0.954 0.946
k141_58166 3037 51.037 9.49 0.989 0.979 0.967
k141_32127 3037 49.325 6.22 0.992 0.979 0.967
k141_2163 3037 26.638 5.05 0.912 0.906 0.897
k141_20839 3036 37.451 8.99 0.971 0.967 0.960
k141_17712 3036 51.713 15.53 0.994 0.988 0.980
Displaying first 5000 of 98799 lines

Table 3 . Basic contig statistics, filtered contigs

Table 3 . Basic contig statistics, filtered contigs
#ID length GC coverage S_2mer S_3mer S_4mer
k141_79676 349967 48.068 72.00 0.995 0.992 0.988
k141_56136 343211 43.778 14.94 0.991 0.987 0.984
k141_20564 342700 47.468 52.60 0.995 0.992 0.988
k141_40847 334399 46.144 42.71 0.995 0.992 0.988
k141_43827 330256 56.528 44.79 0.985 0.976 0.971
k141_13782 299237 42.963 103.09 0.989 0.986 0.983
k141_76730 298100 46.238 47.37 0.994 0.991 0.987
k141_22633 297381 42.899 23.73 0.989 0.985 0.982
k141_60340 294754 53.046 25.60 0.995 0.991 0.988
k141_78371 290324 43.567 16.03 0.992 0.988 0.985
k141_71004 284177 59.188 15.97 0.980 0.976 0.971
k141_90065 260876 51.135 89.77 0.996 0.993 0.990
k141_78110 260643 47.584 52.43 0.996 0.992 0.989
k141_70850 254313 51.474 85.53 0.996 0.993 0.989
k141_75623 250564 60.446 19.20 0.977 0.972 0.968
k141_96938 248942 44.240 18.89 0.991 0.986 0.982
k141_81434 247194 49.716 82.27 0.996 0.993 0.990
k141_45924 244785 45.550 45.20 0.993 0.990 0.986
k141_83169 237758 59.367 18.56 0.980 0.976 0.971
k141_30602 231031 43.386 20.94 0.990 0.986 0.983
k141_2697 219676 44.398 17.16 0.991 0.986 0.982
k141_45404 216979 42.710 95.58 0.987 0.984 0.981
k141_84618 215400 45.105 48.78 0.991 0.988 0.984
k141_46326 214267 51.571 24.92 0.995 0.991 0.987
k141_50301 212672 45.597 23.30 0.994 0.989 0.985
k141_53297 212293 42.071 14.27 0.988 0.985 0.982
k141_32521 210510 48.181 62.49 0.996 0.992 0.989
k141_2074 206891 52.658 24.68 0.994 0.990 0.986
k141_32426 201433 47.380 53.63 0.996 0.992 0.989
k141_47607 197638 42.358 15.11 0.988 0.985 0.982
k141_81143 187223 52.728 25.26 0.995 0.990 0.987
k141_16896 185271 47.826 44.09 0.996 0.993 0.989
k141_50278 183654 59.867 18.78 0.979 0.974 0.970
k141_26236 181034 55.255 37.47 0.987 0.979 0.973
k141_38219 178446 59.119 18.57 0.980 0.975 0.970
k141_33725 176101 52.275 24.71 0.994 0.990 0.986
k141_47217 175853 45.180 13.81 0.995 0.991 0.987
k141_96940 174227 44.188 16.42 0.991 0.987 0.983
k141_5901 173023 42.926 91.57 0.989 0.986 0.983
k141_61816 171919 52.663 64.41 0.996 0.993 0.989
k141_89124 171703 44.804 107.52 0.992 0.989 0.986
k141_96828 170655 55.980 15.15 0.991 0.986 0.982
k141_26037 170476 52.318 24.95 0.996 0.992 0.989
k141_37310 169718 56.888 15.60 0.989 0.985 0.980
k141_75657 166254 54.122 14.82 0.994 0.991 0.987
k141_39818 165793 43.850 24.57 0.990 0.985 0.982
k141_41490 164124 44.333 20.81 0.991 0.986 0.983
k141_77004 163397 43.819 15.31 0.991 0.987 0.984
k141_12153 158681 42.615 17.25 0.988 0.984 0.981
k141_80197 158391 55.843 15.86 0.988 0.984 0.980
k141_22099 157936 51.005 20.89 0.994 0.991 0.987
k141_31338 156971 59.596 14.22 0.979 0.974 0.970
k141_94318 155923 43.156 23.92 0.989 0.985 0.981
k141_51250 154384 46.888 51.21 0.995 0.992 0.988
k141_3877 153662 43.432 90.19 0.990 0.987 0.984
k141_86019 153142 42.690 15.95 0.989 0.984 0.981
k141_32115 152608 42.626 116.31 0.989 0.986 0.983
k141_83416 151527 43.632 15.41 0.990 0.987 0.983
k141_48174 151251 45.147 15.63 0.993 0.989 0.985
k141_27394 150924 44.891 19.95 0.993 0.989 0.985
k141_96398 149946 59.731 15.54 0.979 0.974 0.969
k141_13336 149678 42.159 102.95 0.988 0.985 0.981
k141_43015 149580 47.624 11.12 0.996 0.993 0.990
k141_30027 145668 43.579 107.76 0.990 0.987 0.983
k141_72134 142594 51.029 83.03 0.995 0.992 0.988
k141_44535 142365 41.814 106.19 0.987 0.984 0.981
k141_99310 142287 43.939 98.42 0.991 0.988 0.984
k141_51821 141763 42.184 105.34 0.987 0.984 0.980
k141_53499 136465 56.687 40.01 0.985 0.976 0.970
k141_22152 135305 59.073 17.56 0.979 0.974 0.970
k141_94747 135091 56.597 17.40 0.987 0.982 0.979
k141_27962 133070 49.678 75.86 0.995 0.992 0.988
k141_81870 132617 45.896 22.89 0.993 0.990 0.987
k141_92764 132166 43.645 105.87 0.989 0.986 0.982
k141_23307 132128 42.288 23.56 0.987 0.983 0.979
k141_13083 131559 55.999 43.27 0.985 0.977 0.971
k141_50681 130310 40.701 93.21 0.984 0.981 0.978
k141_59960 130099 43.392 22.82 0.990 0.986 0.982
k141_45737 128383 41.062 21.23 0.984 0.980 0.977
k141_85540 128221 50.585 18.63 0.993 0.989 0.985
k141_57999 125307 50.084 28.18 0.996 0.992 0.989
k141_28310 123846 43.918 18.02 0.992 0.988 0.985
k141_89872 123773 51.598 20.23 0.992 0.988 0.984
k141_50775 122240 58.581 15.35 0.981 0.977 0.972
k141_55237 122187 49.666 17.62 0.992 0.988 0.984
k141_15364 122120 50.239 19.07 0.994 0.990 0.986
k141_10275 121245 49.061 87.40 0.997 0.994 0.990
k141_81344 121088 47.904 50.65 0.996 0.993 0.989
k141_31502 119854 56.956 15.11 0.987 0.984 0.979
k141_60424 119376 41.513 21.66 0.985 0.982 0.978
k141_64681 119182 46.954 21.97 0.995 0.992 0.989
k141_2630 115178 42.488 18.42 0.988 0.985 0.981
k141_24426 113096 52.944 86.49 0.995 0.992 0.987
k141_6559 112992 50.960 82.34 0.995 0.992 0.987
k141_25594 111928 42.784 21.71 0.988 0.983 0.980
k141_87070 111178 57.975 13.71 0.985 0.981 0.977
k141_54479 110986 58.216 13.72 0.983 0.978 0.974
k141_50200 110719 42.642 23.88 0.989 0.985 0.982
k141_56708 109773 47.991 22.63 0.995 0.991 0.988
k141_88900 108052 47.107 50.63 0.995 0.991 0.987
k141_54186 106536 43.321 114.56 0.990 0.986 0.983
k141_5277 105585 53.258 26.77 0.996 0.992 0.988
k141_8948 105435 42.683 94.01 0.988 0.985 0.982
k141_82689 105003 53.173 25.64 0.995 0.991 0.987
k141_24685 104179 48.006 49.06 0.996 0.992 0.987
k141_94157 104038 51.698 87.98 0.997 0.994 0.990
k141_57009 103194 43.400 23.42 0.991 0.987 0.985
k141_93908 103058 42.712 21.32 0.988 0.983 0.980
k141_17039 102470 49.339 77.13 0.996 0.993 0.989
k141_95176 102259 44.355 12.87 0.993 0.989 0.985
k141_18138 102246 51.984 24.47 0.996 0.992 0.989
k141_69352 100834 46.324 47.85 0.993 0.989 0.985
k141_48094 100722 44.054 21.62 0.991 0.986 0.983
k141_69415 100069 42.456 110.80 0.987 0.984 0.981
k141_53630 98611 41.167 19.11 0.986 0.982 0.979
k141_37915 98181 49.633 22.80 0.996 0.993 0.990
k141_6681 97113 49.494 16.79 0.995 0.991 0.988
k141_88514 96475 43.396 22.21 0.990 0.986 0.982
k141_84639 96469 51.144 19.36 0.993 0.990 0.986
k141_13507 95969 54.683 35.04 0.988 0.977 0.971
k141_9597 95611 42.234 20.44 0.986 0.982 0.979
k141_14584 93673 44.244 15.51 0.993 0.989 0.985
k141_35809 92350 54.744 15.64 0.992 0.987 0.983
k141_67258 92210 44.014 118.53 0.992 0.989 0.986
k141_81911 92097 46.030 44.32 0.993 0.990 0.986
k141_69746 91819 51.926 25.14 0.994 0.990 0.986
k141_22542 91155 45.924 48.95 0.992 0.988 0.984
k141_14096 90938 44.397 17.12 0.990 0.985 0.981
k141_54639 90881 48.547 94.18 0.996 0.993 0.989
k141_89902 90510 56.723 41.99 0.983 0.974 0.968
k141_33244 90004 56.279 15.73 0.990 0.985 0.981
k141_35023 89692 53.772 12.20 0.994 0.991 0.988
k141_14260 89188 44.223 91.04 0.991 0.988 0.985
k141_28219 88763 57.652 15.84 0.988 0.984 0.979
k141_89207 87882 42.040 112.11 0.987 0.984 0.981
k141_39746 87627 49.385 80.87 0.996 0.993 0.989
k141_7284 87173 44.237 14.96 0.992 0.988 0.984
k141_77730 86661 43.464 16.92 0.990 0.985 0.982
k141_95440 86515 53.057 23.08 0.993 0.989 0.985
k141_60582 86454 41.184 12.43 0.986 0.982 0.979
k141_3015 86218 51.451 76.30 0.995 0.992 0.987
k141_1375 85903 39.908 19.07 0.980 0.977 0.974
k141_92363 85546 54.970 15.51 0.988 0.983 0.979
k141_77497 85296 55.862 37.53 0.985 0.977 0.970
k141_22752 85138 45.083 111.22 0.992 0.988 0.985
k141_57203 84646 51.024 24.24 0.996 0.992 0.988
k141_40938 84614 35.912 49.67 0.963 0.958 0.954
k141_30743 83402 43.096 21.67 0.989 0.984 0.979
k141_67527 82819 55.090 34.94 0.991 0.987 0.984
k141_98037 82488 41.452 93.19 0.986 0.984 0.981
k141_26772 81974 55.830 35.09 0.986 0.977 0.971
k141_56229 81821 42.415 22.69 0.987 0.983 0.979
k141_53953 81384 64.865 13.76 0.961 0.957 0.951
k141_91152 81285 53.550 22.98 0.992 0.989 0.984
k141_77526 80719 42.082 14.32 0.988 0.984 0.981
k141_39720 80405 58.367 15.37 0.981 0.976 0.971
k141_60627 80387 56.054 14.72 0.986 0.982 0.978
k141_3576 80201 40.598 17.89 0.983 0.980 0.977
k141_34055 79781 41.907 96.18 0.986 0.983 0.980
k141_95640 78463 45.776 18.98 0.994 0.989 0.984
k141_89263 78312 43.320 12.16 0.990 0.987 0.983
k141_43433 78195 46.717 51.46 0.995 0.991 0.987
k141_88748 78120 41.852 22.71 0.986 0.981 0.978
k141_81468 77910 45.975 15.88 0.993 0.989 0.986
k141_54249 77458 44.700 18.74 0.993 0.989 0.985
k141_51897 76803 49.157 44.88 0.996 0.993 0.988
k141_18113 76740 40.658 11.53 0.984 0.981 0.979
k141_32478 76690 43.735 15.42 0.990 0.987 0.983
k141_71745 76151 55.318 44.24 0.987 0.978 0.972
k141_79638 76009 42.990 111.69 0.989 0.986 0.983
k141_52953 75523 56.493 37.27 0.986 0.976 0.970
k141_51909 75121 47.984 22.58 0.995 0.992 0.989
k141_64710 75099 53.561 14.01 0.993 0.988 0.984
k141_60301 74975 49.931 96.87 0.997 0.995 0.991
k141_26687 74893 56.276 43.54 0.983 0.974 0.968
k141_27493 74523 43.733 15.14 0.991 0.987 0.983
k141_81055 74238 41.145 24.37 0.984 0.979 0.976
k141_14149 74029 46.715 15.50 0.996 0.992 0.989
k141_25493 72914 54.493 48.97 0.990 0.981 0.975
k141_9322 71637 43.108 112.72 0.990 0.987 0.984
k141_48164 71566 59.732 17.40 0.979 0.974 0.968
k141_32249 69867 54.332 41.53 0.988 0.981 0.975
k141_65677 69627 55.632 37.32 0.988 0.982 0.977
k141_18413 69550 55.449 37.74 0.988 0.979 0.973
k141_12827 69444 43.459 14.18 0.991 0.988 0.984
k141_53012 69201 42.199 110.97 0.987 0.983 0.980
k141_51651 69195 44.114 29.39 0.990 0.987 0.984
k141_88445 68840 45.521 50.88 0.993 0.990 0.986
k141_10095 68757 41.164 21.58 0.983 0.978 0.975
k141_36755 68739 47.573 74.25 0.995 0.992 0.988
k141_27572 68724 44.840 93.47 0.991 0.987 0.983
k141_86462 68686 59.672 14.21 0.979 0.975 0.970
k141_52365 68586 43.108 11.03 0.990 0.986 0.983
k141_93085 68551 56.476 15.78 0.991 0.987 0.982
k141_82537 68049 52.543 22.47 0.993 0.990 0.986
k141_33726 68038 63.279 16.32 0.967 0.963 0.958
k141_5417 67970 55.463 14.32 0.991 0.988 0.983
k141_33172 67697 56.109 14.36 0.990 0.986 0.982
k141_77538 67417 44.078 22.37 0.990 0.985 0.981
k141_76742 67061 52.691 25.33 0.996 0.992 0.988
k141_12466 66824 53.011 22.48 0.992 0.989 0.985
k141_12476 66786 46.450 12.16 0.993 0.991 0.988
k141_98828 66704 43.903 119.46 0.991 0.988 0.985
k141_85295 65879 42.655 96.64 0.988 0.985 0.981
k141_42895 65839 40.614 15.64 0.982 0.978 0.976
k141_88697 65573 56.209 39.86 0.986 0.977 0.970
k141_46439 65540 44.173 13.96 0.991 0.988 0.985
k141_71615 65169 43.871 20.77 0.992 0.988 0.985
k141_84968 65103 44.376 99.11 0.992 0.988 0.985
k141_99782 64722 42.024 13.05 0.989 0.986 0.984
k141_93010 64415 52.750 11.85 0.992 0.988 0.984
k141_88706 64269 54.319 16.19 0.993 0.989 0.985
k141_90134 63846 47.496 53.99 0.995 0.992 0.988
k141_99790 63740 53.106 12.17 0.992 0.989 0.985
k141_57886 63526 50.530 25.39 0.995 0.992 0.988
k141_1867 63424 56.644 41.35 0.982 0.973 0.966
k141_47519 62988 44.342 15.48 0.991 0.987 0.984
k141_16433 62706 42.956 96.78 0.990 0.988 0.985
k141_25300 62664 43.993 42.57 0.990 0.987 0.984
k141_30595 62354 44.894 12.92 0.993 0.989 0.985
k141_47763 61911 43.231 110.50 0.988 0.985 0.982
k141_870 61831 46.643 102.32 0.994 0.991 0.987
k141_24185 61337 56.460 35.86 0.986 0.978 0.972
k141_88261 61296 55.284 34.65 0.987 0.978 0.972
k141_32084 60876 52.181 24.38 0.994 0.989 0.985
k141_22355 60876 52.592 13.71 0.995 0.990 0.986
k141_8243 60458 41.953 87.96 0.987 0.985 0.981
k141_37615 60443 56.582 35.33 0.986 0.978 0.972
k141_32062 60410 53.038 12.74 0.992 0.988 0.984
k141_17214 60064 58.724 14.25 0.983 0.978 0.973
k141_27961 59867 43.206 23.73 0.989 0.985 0.981
k141_1213 59800 45.682 14.47 0.995 0.990 0.986
k141_62303 59776 43.967 16.21 0.991 0.987 0.983
k141_29045 59458 46.885 30.34 0.995 0.993 0.989
k141_51396 59372 55.351 14.76 0.991 0.987 0.983
k141_13828 59208 42.369 21.45 0.987 0.984 0.980
k141_3440 59040 44.245 14.22 0.993 0.989 0.985
k141_62766 58995 39.720 12.96 0.981 0.977 0.974
k141_90926 58841 43.057 26.65 0.989 0.984 0.981
k141_4477 58656 49.441 76.29 0.995 0.992 0.988
k141_15904 58605 44.111 11.83 0.990 0.986 0.982
k141_54604 58509 42.349 169.28 0.985 0.980 0.974
k141_64794 58491 47.691 46.69 0.995 0.992 0.987
k141_80692 58481 57.391 46.54 0.984 0.975 0.968
k141_24939 58244 47.500 16.60 0.993 0.990 0.986
k141_79875 58051 52.056 20.81 0.990 0.986 0.981
k141_46259 58003 53.904 14.43 0.990 0.985 0.981
k141_54856 57948 53.117 21.86 0.991 0.987 0.983
k141_82900 57717 59.624 14.88 0.978 0.973 0.968
k141_8847 57599 64.234 12.77 0.963 0.958 0.953
k141_31751 57222 42.730 117.38 0.988 0.985 0.982
k141_29637 56919 56.514 40.09 0.984 0.976 0.969
k141_67635 56866 60.894 17.30 0.976 0.972 0.968
k141_34400 56716 41.692 15.54 0.985 0.981 0.978
k141_28092 56616 56.044 36.64 0.986 0.977 0.971
k141_86700 56557 51.842 22.66 0.996 0.992 0.988
k141_56687 56533 60.324 14.89 0.976 0.970 0.964
k141_88790 55816 44.729 17.51 0.992 0.988 0.984
k141_84939 55733 49.606 36.98 0.995 0.989 0.985
k141_67547 55635 59.155 16.11 0.980 0.975 0.970
k141_73915 55279 55.234 37.41 0.985 0.974 0.966
k141_37551 55114 55.944 14.07 0.990 0.985 0.981
k141_49641 55094 47.276 20.65 0.995 0.990 0.986
k141_92176 54927 48.625 54.71 0.996 0.991 0.987
k141_6943 54718 44.742 22.20 0.991 0.987 0.983
k141_19357 54617 41.967 11.30 0.986 0.982 0.978
k141_18481 54341 49.515 32.72 0.986 0.980 0.973
k141_75930 54238 41.379 112.24 0.986 0.983 0.980
k141_49018 54078 46.220 49.88 0.994 0.990 0.986
k141_47661 53749 44.717 51.68 0.992 0.988 0.984
k141_59477 53163 31.731 10.34 0.949 0.946 0.942
k141_39590 53036 62.220 16.69 0.971 0.967 0.962
k141_47187 52537 58.277 15.50 0.986 0.982 0.977
k141_39173 52015 47.075 17.66 0.995 0.990 0.986
k141_21001 51872 58.457 14.43 0.982 0.978 0.973
k141_92419 51794 55.829 13.34 0.990 0.986 0.982
k141_89177 51625 44.922 11.53 0.993 0.989 0.985
k141_67745 51602 43.386 13.93 0.989 0.985 0.982
k141_81550 51555 45.988 47.04 0.994 0.990 0.986
k141_62123 50981 52.280 22.69 0.991 0.988 0.984
k141_6424 50824 48.105 12.25 0.990 0.985 0.981
k141_52669 50459 53.087 23.88 0.995 0.990 0.986
k141_82601 49975 44.276 10.89 0.992 0.988 0.984
k141_77057 49828 56.819 38.76 0.983 0.976 0.971
k141_89774 49751 43.601 15.34 0.990 0.985 0.981
k141_61289 49587 52.869 12.72 0.995 0.991 0.987
k141_43319 49503 42.559 11.44 0.989 0.986 0.983
k141_6435 49297 40.516 20.68 0.984 0.980 0.976
k141_42774 49108 54.058 11.25 0.993 0.991 0.987
k141_89897 49066 43.647 23.05 0.990 0.987 0.983
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k141_24888 3216 54.664 5.55 0.990 0.980 0.967
k141_37294 3215 58.414 4.88 0.976 0.965 0.951
k141_25598 3215 44.572 20.17 0.986 0.979 0.969
k141_70607 3214 44.493 7.30 0.988 0.983 0.975
k141_19262 3213 39.527 7.21 0.979 0.974 0.965
k141_70525 3212 57.472 6.81 0.986 0.976 0.964
k141_5171 3212 43.555 6.43 0.985 0.975 0.966
k141_53010 3211 56.400 7.08 0.988 0.978 0.966
k141_26179 3210 54.237 10.77 0.990 0.982 0.972
k141_70960 3209 36.740 6.76 0.968 0.964 0.957
k141_23613 3209 48.800 7.23 0.978 0.968 0.957
k141_92381 3207 56.657 8.04 0.983 0.972 0.960
k141_7395 3207 55.722 27.00 0.989 0.977 0.963
k141_47018 3207 41.815 70.07 0.986 0.980 0.972
k141_90508 3206 44.635 7.35 0.992 0.987 0.979
k141_88311 3206 52.308 5.14 0.988 0.977 0.966
k141_50121 3206 49.750 23.89 0.996 0.988 0.979
k141_30479 3206 45.945 7.75 0.973 0.962 0.950
k141_58534 3205 57.660 6.25 0.987 0.979 0.969
k141_16680 3204 47.222 6.75 0.994 0.991 0.983
k141_46769 3203 52.264 74.48 0.992 0.987 0.979
k141_23091 3203 54.667 6.99 0.985 0.975 0.963
k141_40952 3202 47.626 12.30 0.993 0.988 0.978
k141_33789 3202 51.187 5.35 0.993 0.984 0.975
k141_12070 3201 54.608 6.31 0.991 0.984 0.974
k141_73393 3200 43.344 5.56 0.986 0.979 0.969
k141_72865 3199 32.698 5.10 0.937 0.931 0.923
k141_59827 3199 43.170 8.27 0.988 0.985 0.977
k141_25506 3199 43.889 15.30 0.985 0.975 0.962
k141_98970 3198 44.028 7.05 0.987 0.982 0.973
k141_90990 3198 45.841 6.67 0.982 0.977 0.968
k141_68292 3198 48.624 7.78 0.993 0.989 0.981
k141_31783 3198 49.625 54.51 0.989 0.983 0.971
k141_61845 3197 53.550 6.42 0.991 0.985 0.975
k141_40431 3197 52.831 6.64 0.994 0.987 0.978
k141_68575 3196 54.850 5.44 0.979 0.969 0.956
k141_4558 3196 41.771 12.46 0.985 0.980 0.971
k141_98148 3195 53.521 9.34 0.978 0.965 0.954
k141_91853 3195 46.792 10.35 0.986 0.977 0.967
k141_31769 3195 49.077 12.94 0.990 0.984 0.972
k141_15514 3195 52.582 5.34 0.992 0.983 0.973
k141_90260 3192 54.292 10.94 0.991 0.981 0.965
k141_65970 3192 47.243 8.92 0.987 0.978 0.968
k141_1336 3191 48.543 9.00 0.994 0.988 0.979
k141_86457 3190 48.025 37.10 0.991 0.986 0.977
k141_85985 3190 36.583 7.88 0.969 0.966 0.960
k141_44949 3190 33.824 39.39 0.950 0.945 0.932
k141_51862 3189 53.998 8.08 0.993 0.985 0.975
k141_38009 3189 53.402 6.83 0.992 0.984 0.974
k141_14385 3189 51.584 17.57 0.993 0.981 0.970
k141_79105 3188 50.376 6.23 0.991 0.982 0.971
k141_60072 3188 50.659 9.10 0.993 0.989 0.980
k141_68695 3187 52.024 8.13 0.992 0.988 0.979
k141_74532 3186 55.681 11.05 0.989 0.983 0.975
k141_63879 3186 55.838 217.95 0.986 0.977 0.966
k141_19007 3185 53.375 5.90 0.989 0.982 0.971
k141_90767 3184 48.964 10.50 0.986 0.974 0.962
k141_76127 3184 56.407 5.38 0.975 0.963 0.950
k141_36618 3184 29.083 6.93 0.928 0.921 0.911
k141_6327 3183 50.927 8.88 0.996 0.988 0.976
k141_4970 3183 38.768 70.54 0.978 0.973 0.966
k141_83404 3181 44.231 6.64 0.991 0.988 0.980
k141_71449 3181 53.568 7.37 0.979 0.967 0.955
k141_40623 3181 50.047 7.21 0.992 0.981 0.971
k141_29429 3181 46.558 14.70 0.981 0.971 0.959
k141_75863 3180 59.403 7.65 0.974 0.961 0.947
k141_98993 3179 42.812 7.28 0.975 0.965 0.953
k141_65147 3179 33.501 4.97 0.946 0.938 0.927
k141_67765 3174 51.103 6.04 0.987 0.976 0.963
k141_47146 3174 38.217 6.71 0.969 0.963 0.955
k141_73591 3173 57.390 7.02 0.980 0.965 0.948
k141_98921 3172 28.846 6.50 0.917 0.905 0.893
k141_27121 3170 46.246 8.39 0.994 0.990 0.983
k141_99035 3169 39.066 7.27 0.970 0.965 0.958
k141_95116 3168 40.278 6.63 0.980 0.975 0.967
k141_70341 3168 62.973 8.96 0.962 0.955 0.944
k141_59757 3167 34.828 16.78 0.959 0.951 0.939
k141_46655 3167 64.888 6.40 0.960 0.953 0.943
k141_86433 3166 40.082 8.96 0.973 0.967 0.960
k141_82086 3166 53.348 7.53 0.986 0.978 0.966
k141_64643 3166 57.517 7.14 0.983 0.974 0.961
k141_63302 3166 37.018 8.01 0.966 0.962 0.955
k141_50365 3166 49.842 6.76 0.994 0.988 0.979
k141_47256 3166 36.260 8.19 0.966 0.959 0.950
k141_87138 3165 54.439 6.22 0.990 0.982 0.971
k141_40261 3165 41.264 8.42 0.982 0.978 0.969
k141_52744 3164 37.579 12.36 0.972 0.968 0.958
k141_495 3164 33.502 5.71 0.941 0.928 0.912
k141_38061 3164 59.893 5.95 0.979 0.969 0.956
k141_6965 3161 49.668 8.89 0.986 0.975 0.963
k141_60194 3160 41.646 5.92 0.968 0.959 0.947
k141_29642 3160 52.184 5.78 0.994 0.987 0.978
k141_88441 3157 53.595 10.70 0.993 0.988 0.978
k141_87383 3157 56.351 7.62 0.988 0.978 0.965
k141_87466 3156 48.606 13.93 0.991 0.985 0.976
k141_44389 3156 41.381 8.02 0.982 0.977 0.968
k141_27966 3155 48.780 15.88 0.991 0.986 0.976
k141_88478 3153 50.682 9.50 0.991 0.977 0.966
k141_54038 3152 58.312 7.90 0.952 0.940 0.926
k141_32502 3152 39.150 7.04 0.974 0.970 0.963
k141_5035 3150 48.667 10.93 0.986 0.976 0.962
k141_93786 3149 53.700 10.52 0.992 0.985 0.973
k141_93492 3149 57.637 6.77 0.983 0.973 0.962
k141_631 3148 50.032 7.25 0.985 0.975 0.966
k141_25668 3148 48.666 5.34 0.992 0.986 0.977
k141_44331 3146 48.093 6.91 0.993 0.988 0.978
k141_67179 3144 49.809 6.14 0.992 0.984 0.975
k141_66648 3144 46.501 8.50 0.990 0.982 0.971
k141_76693 3143 40.757 6.00 0.969 0.963 0.955
k141_39533 3143 50.970 8.26 0.970 0.959 0.946
k141_93614 3142 34.087 5.20 0.958 0.954 0.945
k141_20674 3142 38.701 6.25 0.978 0.974 0.968
k141_38716 3140 48.280 6.01 0.993 0.981 0.969
k141_96659 3138 52.199 21.57 0.986 0.980 0.970
k141_22775 3138 37.412 7.13 0.971 0.968 0.962
k141_19429 3138 50.319 11.32 0.995 0.991 0.983
k141_99122 3137 33.918 6.51 0.946 0.935 0.921
k141_57969 3136 41.295 8.32 0.979 0.974 0.965
k141_43389 3136 38.616 9.72 0.973 0.969 0.960
k141_86593 3135 37.448 6.62 0.968 0.963 0.956
k141_33782 3135 46.061 10.48 0.990 0.981 0.972
k141_31099 3135 48.644 5.09 0.993 0.985 0.975
k141_60836 3134 42.597 6.81 0.988 0.983 0.974
k141_98575 3133 50.686 12.68 0.996 0.991 0.981
k141_2601 3133 55.027 9.32 0.972 0.958 0.945
k141_61951 3130 51.693 7.94 0.987 0.979 0.970
k141_93506 3129 47.299 6.84 0.992 0.982 0.971
k141_56720 3129 50.815 13.65 0.990 0.979 0.967
k141_65124 3127 52.638 5.15 0.995 0.987 0.976
k141_56683 3125 51.584 5.73 0.994 0.985 0.976
k141_44774 3124 46.959 34.26 0.995 0.986 0.977
k141_93873 3123 43.612 7.89 0.985 0.977 0.968
k141_87975 3123 48.415 6.04 0.982 0.972 0.956
k141_17362 3123 47.711 89.89 0.995 0.992 0.985
k141_84151 3122 53.812 27.29 0.985 0.975 0.962
k141_21883 3122 52.819 8.26 0.981 0.969 0.958
k141_73879 3121 32.426 5.79 0.940 0.931 0.919
k141_8945 3119 41.969 6.25 0.987 0.982 0.974
k141_37328 3119 43.988 15.73 0.989 0.984 0.977
k141_12722 3118 33.900 7.78 0.940 0.936 0.928
k141_95273 3117 55.951 7.50 0.984 0.974 0.963
k141_91952 3116 53.049 10.37 0.991 0.985 0.976
k141_14693 3116 50.513 10.60 0.994 0.987 0.976
k141_61567 3115 50.883 7.71 0.996 0.989 0.979
k141_643 3114 51.541 7.08 0.993 0.983 0.971
k141_32332 3114 30.379 5.63 0.928 0.915 0.901
k141_63174 3113 52.168 9.99 0.991 0.986 0.976
k141_7884 3112 43.766 21.07 0.986 0.976 0.965
k141_89507 3111 42.269 8.99 0.986 0.979 0.970
k141_39597 3111 40.791 8.82 0.983 0.979 0.971
k141_29683 3111 37.287 6.02 0.961 0.954 0.944
k141_80071 3110 54.952 6.37 0.985 0.975 0.964
k141_34538 3110 40.611 8.05 0.981 0.977 0.969
k141_22676 3110 63.215 11.73 0.962 0.952 0.939
k141_46574 3109 46.156 9.72 0.990 0.981 0.971
k141_63639 3108 53.057 8.14 0.995 0.990 0.982
k141_40169 3108 47.426 6.33 0.988 0.983 0.974
k141_97433 3107 47.473 8.00 0.978 0.968 0.957
k141_50028 3107 55.391 5.12 0.984 0.976 0.965
k141_68821 3106 52.737 5.45 0.990 0.980 0.970
k141_34730 3105 40.676 12.05 0.974 0.969 0.960
k141_85648 3104 35.599 6.40 0.957 0.948 0.938
k141_70602 3103 53.432 22.04 0.985 0.977 0.968
k141_19385 3103 25.814 16.30 0.904 0.901 0.894
k141_20975 3102 52.257 6.36 0.990 0.980 0.968
k141_77616 3101 37.955 7.91 0.958 0.953 0.945
k141_16998 3100 46.613 6.81 0.985 0.977 0.965
k141_77685 3098 56.230 34.01 0.981 0.977 0.969
k141_73826 3097 46.529 9.92 0.984 0.973 0.961
k141_42845 3097 52.050 11.18 0.988 0.977 0.964
k141_25050 3097 54.246 8.99 0.976 0.964 0.952
k141_2417 3097 58.541 12.72 0.979 0.969 0.958
k141_193 3097 46.787 12.56 0.992 0.982 0.971
k141_67991 3096 41.505 8.74 0.972 0.966 0.958
k141_5437 3096 50.904 8.23 0.993 0.987 0.979
k141_30864 3095 49.273 6.60 0.986 0.977 0.967
k141_21889 3095 53.376 7.70 0.992 0.986 0.976
k141_6207 3094 32.321 5.01 0.943 0.934 0.921
k141_1924 3094 48.578 8.18 0.994 0.990 0.982
k141_41710 3093 48.400 6.82 0.980 0.973 0.963
k141_55639 3092 50.291 5.70 0.991 0.984 0.974
k141_53755 3092 48.836 31.22 0.995 0.990 0.982
k141_60911 3091 51.019 7.93 0.980 0.965 0.951
k141_5155 3091 49.790 8.79 0.979 0.969 0.958
k141_40506 3091 31.834 6.55 0.934 0.922 0.910
k141_25596 3091 56.519 6.26 0.977 0.960 0.944
k141_51422 3090 48.803 7.47 0.993 0.987 0.976
k141_50864 3090 60.032 7.03 0.974 0.963 0.951
k141_48926 3090 46.278 11.09 0.991 0.981 0.969
k141_3329 3090 59.482 5.31 0.977 0.967 0.953
k141_76230 3089 51.182 10.28 0.985 0.974 0.961
k141_40537 3089 46.811 7.64 0.989 0.979 0.969
k141_27845 3089 50.049 7.06 0.993 0.985 0.975
k141_91009 3087 35.536 7.03 0.964 0.961 0.955
k141_77820 3087 53.320 6.70 0.989 0.981 0.969
k141_28580 3087 53.191 6.95 0.993 0.984 0.973
k141_21142 3087 47.133 7.82 0.984 0.979 0.970
k141_6113 3086 52.333 6.52 0.989 0.980 0.970
k141_5790 3086 37.492 7.45 0.966 0.958 0.949
k141_44978 3086 51.167 7.92 0.989 0.984 0.975
k141_15689 3086 46.111 7.79 0.991 0.987 0.980
k141_94608 3085 46.872 10.30 0.993 0.988 0.979
k141_29145 3085 49.627 11.15 0.989 0.986 0.978
k141_22803 3085 51.183 4.02 0.995 0.987 0.975
k141_87051 3084 41.991 14.96 0.986 0.982 0.974
k141_13319 3084 47.795 6.41 0.982 0.975 0.965
k141_28394 3082 36.729 8.74 0.966 0.963 0.956
k141_1418 3082 52.920 5.74 0.992 0.985 0.976
k141_43362 3081 56.378 6.21 0.983 0.975 0.964
k141_95489 3080 54.610 6.42 0.991 0.984 0.974
k141_99284 3079 51.120 7.66 0.987 0.980 0.971
k141_15858 3079 44.755 9.38 0.990 0.985 0.976
k141_23095 3078 49.838 7.01 0.995 0.983 0.972
k141_84263 3076 51.463 11.84 0.993 0.984 0.973
k141_83647 3076 53.316 6.23 0.991 0.984 0.971
k141_15763 3076 48.147 7.35 0.993 0.989 0.981
k141_69554 3075 49.431 31.04 0.989 0.983 0.974
k141_52242 3075 40.943 24.12 0.971 0.966 0.957
k141_19316 3075 56.780 7.10 0.974 0.967 0.957
k141_11070 3075 38.276 5.41 0.975 0.973 0.966
k141_68130 3074 29.506 8.12 0.933 0.930 0.922
k141_1456 3074 59.889 8.62 0.976 0.966 0.952
k141_6402 3073 38.432 10.20 0.971 0.966 0.957
k141_33006 3073 51.025 4.62 0.993 0.986 0.976
k141_8706 3072 43.294 7.44 0.986 0.981 0.973
k141_72352 3071 31.879 6.64 0.942 0.935 0.924
k141_57383 3071 58.808 10.40 0.982 0.975 0.965
k141_49143 3071 38.294 213.48 0.971 0.963 0.950
k141_72919 3070 38.143 7.06 0.973 0.970 0.963
k141_3107 3070 53.941 6.39 0.983 0.972 0.958
k141_24544 3070 56.352 7.93 0.986 0.981 0.971
k141_12203 3070 42.508 6.31 0.985 0.979 0.972
k141_76082 3069 53.926 10.27 0.992 0.988 0.980
k141_47562 3069 51.450 7.17 0.994 0.987 0.976
k141_31034 3069 55.002 8.07 0.981 0.966 0.953
k141_74947 3068 37.647 6.97 0.974 0.971 0.963
k141_72585 3068 48.142 8.86 0.989 0.979 0.969
k141_56129 3068 43.383 38.94 0.985 0.977 0.965
k141_39435 3068 60.267 5.69 0.968 0.958 0.946
k141_14881 3067 40.985 6.48 0.978 0.972 0.963
k141_90901 3066 52.511 7.46 0.983 0.977 0.968
k141_57052 3066 49.772 8.88 0.993 0.983 0.974
k141_90529 3064 43.342 9.48 0.970 0.964 0.956
k141_86018 3064 50.033 7.31 0.987 0.974 0.961
k141_4922 3064 46.736 37.90 0.986 0.982 0.974
k141_18076 3064 40.960 9.92 0.983 0.978 0.970
k141_39765 3063 48.482 8.32 0.991 0.987 0.980
k141_23273 3063 52.628 6.51 0.991 0.981 0.970
k141_90576 3062 46.571 16.25 0.989 0.985 0.977
k141_86272 3062 28.805 9.88 0.930 0.927 0.921
k141_17440 3062 50.425 15.04 0.986 0.980 0.973
k141_92129 3061 35.707 4.59 0.962 0.958 0.950
k141_19315 3061 34.237 6.81 0.945 0.934 0.921
k141_86104 3060 50.196 20.11 0.982 0.973 0.959
k141_71706 3060 60.033 6.15 0.971 0.958 0.941
k141_84340 3059 58.320 7.43 0.980 0.969 0.953
k141_43535 3059 44.426 17.54 0.988 0.980 0.969
k141_26507 3058 38.522 7.54 0.975 0.971 0.962
k141_49582 3057 63.363 15.74 0.964 0.954 0.940
k141_79484 3056 45.746 5.08 0.987 0.975 0.964
k141_71307 3056 38.973 7.76 0.968 0.964 0.957
k141_66971 3056 54.777 11.82 0.986 0.978 0.968
k141_10941 3056 42.866 25.29 0.989 0.985 0.975
k141_95180 3055 46.972 5.64 0.993 0.984 0.974
k141_67657 3055 49.034 15.92 0.994 0.989 0.980
k141_47920 3055 41.538 6.86 0.970 0.962 0.952
k141_61706 3054 50.524 6.25 0.993 0.982 0.970
k141_72878 3052 41.776 4.78 0.956 0.948 0.935
k141_43970 3052 54.161 29.05 0.989 0.981 0.971
k141_35606 3050 46.918 438.52 0.991 0.985 0.974
k141_41719 3049 47.557 20.32 0.982 0.975 0.961
k141_38019 3049 32.765 6.23 0.953 0.950 0.944
k141_14528 3049 59.134 8.33 0.983 0.973 0.960
k141_40055 3048 47.605 5.26 0.991 0.986 0.976
k141_23410 3048 33.924 13.26 0.938 0.925 0.910
k141_97986 3047 29.865 5.85 0.928 0.919 0.908
k141_2448 3047 52.872 6.64 0.994 0.985 0.973
k141_66865 3046 47.045 5.78 0.988 0.977 0.966
k141_31957 3045 43.810 27.75 0.984 0.977 0.966
k141_22813 3045 51.067 7.17 0.982 0.971 0.959
k141_14661 3045 45.025 7.58 0.990 0.984 0.975
k141_23499 3044 55.618 5.28 0.970 0.963 0.949
k141_49334 3043 41.702 209.36 0.971 0.959 0.947
k141_12987 3043 45.613 7.45 0.990 0.984 0.975
k141_14085 3042 58.481 6.26 0.972 0.959 0.944
k141_1458 3041 48.767 6.30 0.983 0.971 0.960
k141_66712 3039 54.623 6.84 0.989 0.980 0.969
k141_39704 3039 44.620 7.50 0.989 0.984 0.975
k141_46206 3038 55.300 8.40 0.991 0.987 0.979
k141_99207 3037 53.902 14.79 0.987 0.979 0.968
k141_9693 3037 46.526 5.22 0.983 0.970 0.958
k141_84351 3037 52.453 11.29 0.985 0.974 0.963
k141_74134 3037 34.705 5.91 0.959 0.954 0.946
k141_58166 3037 51.037 10.25 0.989 0.979 0.967
k141_32127 3037 49.325 6.22 0.992 0.979 0.967
k141_2163 3037 26.638 5.20 0.912 0.906 0.897
k141_20839 3036 37.451 9.06 0.971 0.967 0.960
k141_17712 3036 51.713 36.87 0.994 0.988 0.980
Displaying first 5000 of 17839 lines

Method: eggNOG MapperCarlos P. Cantalapiedra, Ana Hernandez-Plaza, Ivica Letunic, Peer Bork, Jaime Huerta-Cepas. (2021). eggNOG-mapper v2: functional annotation, orthology assignments, and domain prediction at the metagenomic scale. Molecular Biology and Evolution, msab293, https://doi.org/10.1093/molbev/msab293

Fast genome-wide functional annotation through orthology assignment.

Custom Parameters
  • --itype = metagenome
  • --genepred = prodigal
  • --decorate_gff = yes
Method: Bacterial ribosomal RNA predictor Seemann T. barrnap 0.9 : rapid ribosomal RNA prediction https://github.com/tseemann/barrnap

Barrnap predicts the location of ribosomal RNA genes in genomes. It supports bacteria (5S,23S,16S), archaea (5S,5.8S,23S,16S), metazoan mitochondria (12S,16S) and eukaryotes (5S,5.8S,28S,18S).

Custom Parameters
  • --outseq = alldata_annotation_rrna.fa
Method: ARAGORNLaslett D, Canback B. (2004) ARAGORN, a program to detect tRNA genes and tmRNA genes in nucleotide sequences. Nucleic Acids Res. 32(1):11-16. https://doi.org/10.1093/nar/gkh152

a program to detect tRNA genes and tmRNA genes in nucleotide sequences

Custom Parameters
  • -m
  • -t
  • -l
  • -w
  • -seq
Method: CAT/BAT: tool for taxonomic classification of contigs and metagenome-assembled genomes (MAGs) von Meijenfeldt FAB, Arkhipova K, Cambuy DD, Coutinho FH, Dutilh BE. (2019) Robust taxonomic classification of uncharted microbial sequences and bins with CAT and BAT. Genome Biology. 20:217. https://doi.org/10.1186/s13059-019-1817-x

Contig Annotation Tool (CAT) and Bin Annotation Tool (BAT) are pipelines for the taxonomic classification of long DNA sequences and metagenome assembled genomes (MAGs/bins) of both known and (highly) unknown microorganisms, as generated by contemporary metagenomics studies.

Custom Parameters
  • database = /mmfs1/projects/rrc_shared/common/references/CAT/20240422_CAT_nr/db
  • taxonomy = /mmfs1/projects/rrc_shared/common/references/CAT/20240422_CAT_nr/tax

Table 1 . Annotation details

Table 1 . Annotation details
Feature Counts
CDS 93520
rRNA 115
tmRNA 17
tmRNA* 4
tRNA 1181

  • Binning assembled contigs

Method: MetaBAT2Kang DD, Li F, Kirton E, Thomas A, Egan R, An H, Wang Z. (2019) MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ. 7:e7359. doi: 10.7717/peerj.7359. PMID: 31388474; PMCID: PMC6662567.

A robust statistical framework for reconstructing genomes from metagenomic data

Custom Parameters
  • --saveCls
  • -l
Method: CAT/BAT: tool for taxonomic classification of contigs and metagenome-assembled genomes (MAGs) von Meijenfeldt FAB, Arkhipova K, Cambuy DD, Coutinho FH, Dutilh BE. (2019) Robust taxonomic classification of uncharted microbial sequences and bins with CAT and BAT. Genome Biology. 20:217. https://doi.org/10.1186/s13059-019-1817-x

Contig Annotation Tool (CAT) and Bin Annotation Tool (BAT) are pipelines for the taxonomic classification of long DNA sequences and metagenome assembled genomes (MAGs/bins) of both known and (highly) unknown microorganisms, as generated by contemporary metagenomics studies.

Custom Parameters
  • database = /mmfs1/projects/rrc_shared/common/references/CAT/20240422_CAT_nr/db
  • taxonomy = /mmfs1/projects/rrc_shared/common/references/CAT/20240422_CAT_nr/tax

Citations